Machine intelligence is changing the way we study and understand mental illness

Psychiatry, the study and prevention of mental disorders, is currently undergoing a quiet revolution. For decades, even centuries, this discipline has been based largely on subjective observation. Large-scale studies have been hampered by the difficulty of objectively assessing human behavior and comparing it with a well-established norm. Just as tricky, there are few well-founded models of neural circuitry or brain biochemistry, and it is difficult to link this science with real-world behavior.

That has begun to change thanks to the emerging discipline of computational psychiatry, which uses powerful data analysis, machine learning, and artificial intelligence to tease apart the underlying factors behind extreme and unusual behaviors.    

Computational psychiatry has suddenly made it possible to mine data from long-standing observations and link it to mathematical theories of cognition. It’s also become possible to develop computer-based experiments that carefully control environments so that specific behaviors can be studied in detail.

How is this new-fangled science influencing researchers’ understanding of mental illness? Today we get an answer of sorts, thanks to the work of Sarah Fineberg and colleagues at Yale University in New Haven.

Fineberg and co review the impact that computational psychiatry is having on the study of borderline personality disorder, a condition that affects almost 2 percent of the population at any time. They show that the field is profoundly influencing the way mental-health professionals study and diagnose this affliction.

Borderline personality disorder is characterized by an inability to form stable relationships, an unstable sense of self, and unstable emotions. People with this diagnosis are significantly more likely to harm themselves, and some 10 percent commit suicide.

The cause of borderline personality disorder is not known. But a wide range of genetic, environmental, and social factors seem to play a role. As a result, characterizing the condition is still a challenge. But computational approaches are beginning to help.

A good example is the computer game Cyberball, which measures social rejection. The game involves three computerized players passing a ball back and forth on a screen. The subject controls one of the players, thinking that other people are controlling the other two. In reality, the other players are computer-controlled.

A key feature of the game is that unbeknownst to the subject, researchers can control how often the subject receives the ball. “By varying the percentage of the time the ball is passed to the participant, feelings of social rejection can be evoked,” say Fineberg and co.

In the most extreme case, the subject passes the ball to another of the players, and they then pass it between themselves for the rest of the game. “This experience elicits sadness and anger in as few as six rounds of play,” say Fineberg and co. That allows researchers to study how these feelings differ between people with and without borderline personality disorder.